Secondary insults prevalence, co-occurrence and relationship with outcome after severe TBI
Bibliographic record
Abstract
Introduction: Secondary insults due to high intracranial pressure (ICP), low cerebral perfusion pressure (CPP) and impaired cerebral pressure reactivity (PRx) predict outcome after severe traumatic brain injury (TBI). Research question: What is the prevalence, co-occurrence and prognostic importance of secondary insults due to deranged ICP, CPP or PRx after TBI. Material and methods: Severe TBI patients requiring ICP monitoring were included. Secondary insults due to ICP, PRx, and CPP were defined as having at least 1 h with a mean value above (or below for CPP) a respective threshold (ICP 20, CPP 60, and PRx 0.25). Percentage time with isolated or co-occurring insults was calculated (impaired ICP only, CPP only, PRx only, ICP and PRx, ICP and CPP, CPP and PRx, ICP CPP and PRx). Prognostic importance for mortality was assessed using a logistic regression model. Results: 822 patients were included of which 76% had elevated ICP, 92% had disturbed pressure reactivity and 55% had low CPP for at least an hour. Out of the total 115,459 h, 46,111 (40%) were spent with at least one variable within the defined secondary injury range. Odds ratios for mortality were greater for combined (impaired ICP, CPP and PRx OR 1.17 95%CI 1.09 to 1.28) than isolated insults (impaired ICP only OR 1.01 95%CI 1.00-1.02, impaired CPP only 1.00 95%CI 0.95-1.05). Discussion and conclusion: ICP and autoregulation insults are common after TBI and often occur independently. Concurrent ICP, CPP and PRx insults portend worse prognosis than when a single variable is deranged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".